In recent years, intensive water use combined with global climate change has increased fluctuations in freshwater lake levels, hydrological characteristics, water quality, and water ecosystem balance. To provide a sustainable management plan in the long term, deep learning models (DL) can provide fast and reliable predictions of lake water levels (LWLs) in challenging future scenarios. In this study, artificial neural networks (ANNs) and four recurrent neural network (RNN) algorithms were investigated to predict LWLs that were applied in time series such as one day, five days, ten days, twenty days, one month, two months, and four months ahead. The results show that the performance of the Long Short-Term Memory (LSTM) model with a prediction of 60 days is in the very good range and outperforms the benchmark, the Naïve Method, by 78% and the ANN at the significance level (p < 0.05) with an RMSE = 0.1762 compared to other DL algorithms. The RNN-based DL algorithms show better prediction performance, specifically, for long time horizons, 57.98% for 45 days, 78.55% for 60 days, and 58% for 120 days, and it is better to use a prediction period of at least 20 days with an 18.45% performance increase to take advantage of the gated RNN algorithms for predicting future water levels. Additionally, microcystin concentration was tightly correlated with temperature and was most elevated between 15 and 20 m water depths during the summer months. Evidence on LWL forecasting and microcystin concentrations in the context of climate change could help develop a sustainable water management plan and long-term policy for drinking water lakes.

Air pollution poses significant environmental and public health challenges, especially in urban–industrial areas where pollutant dynamics are influenced by complex interactions with meteorological factors. This study examines the seasonal variations and correlations between air pollutants (PM₁₀, NO, NO₂, and CO) and meteorological parameters (wind speed, temperature, relative humidity, and rainfall) in Sakarya, Türkiye, in 2021–2023. Statistical analyses and predictive models, including multiple linear regression (MLR) and random forest (RF), were applied to evaluate the factors shaping pollutant levels and assess model effectiveness in forecasting air quality. The findings highlight wind speed and rainfall as critical in reducing PM₁₀ and NO concentrations, with notable seasonal effects. RF outperformed MLR for PM₁₀ predictions, while MLR better captured the linear relationships influencing NO and NO₂ levels. Both models faced challenges in predicting CO due to its diverse sources and weak meteorological links. The dynamic effects of temperature and relative humidity further emphasize the complexity of pollutant behavior. This research underscores the necessity of integrating meteorological data into air quality strategies and provides actionable recommendations for policymakers and urban planners to advance sustainable urban development.